Explainable machine learning (ML) enables human learning from ML, human appeal of automated model decisions, regulatory compliance, and security audits of ML models.This text and associated software are not, and should not be construed as, legal advice or requirements for regulatory compliance.,In the U.S., interpretable models, explanations, disparate impact testing, and the model documentation they enable may be required under the Civil Rights Acts of 1964 and 1991, the Americans with Disabilities Act, the Genetic Information Nondiscrimination Act, the Health Insurance Portability and Accountability Act, the Equal Credit Opportunity Act (ECOA), the Fair Credit Reporting Act (FCRA), the Fair Housing Act, Federal Reserve SR 11-7, and the European Union (E.U.) Greater Data Privacy Regulation (GDPR) Article 22 [1]}.,For various security applications, see: “Proposals for Model Vulnerability and Security”. Explainable ML (i.e. explainable artificial intelligence or XAI) has been implemented in numerous open source and commercial packages and explainable ML is also an important, mandatory, or embedded aspect of commercial predictive modeling in industries like financial services.E.g. open source software listed here: “Awesome machine learning interpretability”.,E.g., Datarobot, H2O Driverless AI, SAS Visual Data Mining and Machine Learning, Zest AutoML.,E.g., “Deep Insights into Explainability and Interpretability of Machine Learning Algorithms and Applications to Risk Management”. However, like many technologies, explainable ML can be misused, particularly as a faulty safeguard for harmful black-boxes, e.g. fairwashing or scaffolding, and for other malevolent purposes like stealing models and sensitive training data [2]}, [3]}, [4]}, [5]}, [6]}. To promote best-practice discussions for this already in-flight technology, this short text presents internal definitions and a few examples in Section  before covering the proposed guidelines in Subsections REF  – REF . This text concludes in Section  with a seemingly natural argument for the use of interpretable models and explanatory, debugging, and disparate impact testing methods in life- or mission-critical ML systems.
